Pub Date : 2019-12-12DOI: 10.33422/researchconf.2019.12.898
Mehmet Umit Ak, S. Bilgin
. There are some studies in the literature in order to make the interpretation of human tissues having different characteristics. Some of these studies focused on the evaluation of human face tissues. Vibration signals generated from vocal cords have been used in these studies about human face tissues. However, any study using the vibration signals recorded by applying the external vibration source having fixed frequency value is not available in the literature. In this study, it is aimed to investigate the frequency characteristics of the vibration signals recorded from human face. These signals obtained from 9 different regions on the faces of subjects are analysed using frequency characteristics. In the analysis stage, median and maximum frequency values are calculated and evaluated. So, the softness and hardness interpretation about these regions on the face can be made and the frequency ranges of these regions can be determined. As a result, it is observed that low frequency signals are dominant in hard regions and high frequency signals are dominant in soft regions.
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Pub Date : 2019-12-12DOI: 10.33422/researchconf.2019.12.897
Eunpil Kim
Free convection characteristics are investigated in a vertical minichannel with two grooves. The wall geometry of the minichannel has grooved shapes with several modified groove ratios. To find the effects of natural convective heat transfer, the finite volume numerical method is used. For the case of two grooves, there are four flow separations at d/D = 0.5. Heat transfer patterns are similar to the single groove case. However, at the ending section of the first groove temperatures spread out to the downstream. In the case of the 1 mm distance case between grooves, the Nusselt number shows larger variation compared to the other cases. When the channel pitch is 3 mm, the position location between grooves is not a large factor after 2 mm groove distance.
研究了双沟槽垂直小通道内的自由对流特性。微型通道的壁面几何形状具有若干修正的沟槽比的沟槽形状。为了研究自然对流换热的影响，采用了有限体积数值方法。对于两个凹槽的情况，在d/ d = 0.5时有四个流动分离。传热模式类似于单槽壳体。然而，在第一槽的末端，温度向下游扩散。在凹槽之间距离为1mm的情况下，与其他情况相比，努塞尔数显示出更大的变化。当槽距为3mm时，槽距为2mm后槽间的位置位置影响不大。
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Pub Date : 2019-12-12DOI: 10.33422/researchconf.2019.12.894
G. Georgieva-Tsaneva, Mitko Valchev Gospodinov, Evgeniya Peneva Gospodinova
The paper discusses spectral methods for analyzing heart rate variability, which is a dynamic, non-stationary variable. Today the analysis of heart rate with mathematical methods is a current task as diseases of the cardiovascular system, and disability and mortality in humans as a result of them are very common worldwide. In our modern age, technologies need to be of assistance to medicine and to help both improve the health of individuals and take appropriate preventive measures to protect the health of people. Electrocardiography and long-term monitoring of Holter are well-established, non-invasive medical methods for cardiovascular activity testing. Spectral analysis of the heart rate variability makes it possible to evaluate the work of the heart and to give an estimate of its condition in the coming days. Spectral analysis of variability is performed in three frequency ranges and can be done with different mathematical methods. This paper uses a Welch periodogram method to analyze records of healthy individuals and patients with arrhythmias. The analysis was performed on real Holter continuous cardiac records on patients with proven cardiac disease, diagnosed by a cardiologist, and on people without cardiovascular problems. The presented numerical and graphical results are obtained using the MATLAB software program, created by the authors. The comparative analyses show differences in the studied frequency parameters between patients with arrhythmia and healthy individuals. The conducted research and the obtained results can be useful in the clinical practice of cardiologists.
{"title":"Spectral Analysis of Heart Rate Variability of Holter Records","authors":"G. Georgieva-Tsaneva, Mitko Valchev Gospodinov, Evgeniya Peneva Gospodinova","doi":"10.33422/researchconf.2019.12.894","DOIUrl":"https://doi.org/10.33422/researchconf.2019.12.894","url":null,"abstract":"The paper discusses spectral methods for analyzing heart rate variability, which is a dynamic, non-stationary variable. Today the analysis of heart rate with mathematical methods is a current task as diseases of the cardiovascular system, and disability and mortality in humans as a result of them are very common worldwide. In our modern age, technologies need to be of assistance to medicine and to help both improve the health of individuals and take appropriate preventive measures to protect the health of people. Electrocardiography and long-term monitoring of Holter are well-established, non-invasive medical methods for cardiovascular activity testing. Spectral analysis of the heart rate variability makes it possible to evaluate the work of the heart and to give an estimate of its condition in the coming days. Spectral analysis of variability is performed in three frequency ranges and can be done with different mathematical methods. This paper uses a Welch periodogram method to analyze records of healthy individuals and patients with arrhythmias. The analysis was performed on real Holter continuous cardiac records on patients with proven cardiac disease, diagnosed by a cardiologist, and on people without cardiovascular problems. The presented numerical and graphical results are obtained using the MATLAB software program, created by the authors. The comparative analyses show differences in the studied frequency parameters between patients with arrhythmia and healthy individuals. The conducted research and the obtained results can be useful in the clinical practice of cardiologists.","PeriodicalId":346784,"journal":{"name":"Proceedings of The International Conference on Research in Engineering and Technology","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130935707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-12DOI: 10.33422/researchconf.2019.12.895
Evgeniya Gospodinova
This article is devoted to the fractal analysis of the intervals between heart beats (RR intervals) obtained from electrocardiographical signals. The following methods are used to determine the fractal behavior of the studied signals by the Hurst exponent: Rescaled range, wavelet method, Detrended Fluctuation Analysis. The Hurst exponent value determined by the proposed methods depends on a number of factors: the estimation method, the size of the data, the type of wavelet function, etc. To solve the problem associated with finding the optimal Hurst method, fractal Gaussian noise (FGN) was simulated with different inputs of the Hurst exponent (0.6, 0.7, 0.8, 0.9) and with different data lengths (1000, 10000, 100000). The testing results of the accuracy of the Hurst exponent when applying those three methods is that at a data length of 100000 points, the relative error of the Hurst exponent is the smallest. The Detrended Fluctuation Analysis and wavelet method for estimating the Hurst exponents with respect to the precision parameter have a relative error of less than 1.4%. These two methods have been applied to examine the Holter recordings of two groups of people: healthy and unhealthy subjects. The results show that the Hurst values in healthy and diseased individuals differ. Another marker used to distinguish between the two groups is the generalized Hurst exponent, with diseased subjects having monofractal behavior and healthy subjects-multifractal. In the conclusion, based on the obtained results, it follows that fractal analysis is appropriate for estimating the fuction state of the human body.
{"title":"Hurst Methods for Fractal Analysis of Electrocardiographical Signals","authors":"Evgeniya Gospodinova","doi":"10.33422/researchconf.2019.12.895","DOIUrl":"https://doi.org/10.33422/researchconf.2019.12.895","url":null,"abstract":"This article is devoted to the fractal analysis of the intervals between heart beats (RR intervals) obtained from electrocardiographical signals. The following methods are used to determine the fractal behavior of the studied signals by the Hurst exponent: Rescaled range, wavelet method, Detrended Fluctuation Analysis. The Hurst exponent value determined by the proposed methods depends on a number of factors: the estimation method, the size of the data, the type of wavelet function, etc. To solve the problem associated with finding the optimal Hurst method, fractal Gaussian noise (FGN) was simulated with different inputs of the Hurst exponent (0.6, 0.7, 0.8, 0.9) and with different data lengths (1000, 10000, 100000). The testing results of the accuracy of the Hurst exponent when applying those three methods is that at a data length of 100000 points, the relative error of the Hurst exponent is the smallest. The Detrended Fluctuation Analysis and wavelet method for estimating the Hurst exponents with respect to the precision parameter have a relative error of less than 1.4%. These two methods have been applied to examine the Holter recordings of two groups of people: healthy and unhealthy subjects. The results show that the Hurst values in healthy and diseased individuals differ. Another marker used to distinguish between the two groups is the generalized Hurst exponent, with diseased subjects having monofractal behavior and healthy subjects-multifractal. In the conclusion, based on the obtained results, it follows that fractal analysis is appropriate for estimating the fuction state of the human body.","PeriodicalId":346784,"journal":{"name":"Proceedings of The International Conference on Research in Engineering and Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122214400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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